SSD is an unified framework for object detection with a single network.

You can use the code to train/evaluate/test for object detection task.

This is a re-implementation of original SSD which is based on caffe. The official repository is available here. The arXiv paper is available here.

This example is intended for reproducing the nice detector while fully utilize the remarkable traits of MXNet.

• The model is fully compatible with caffe version.
• Model converter from caffe is available now!
• The result is almost identical to the original version. However, due to different implementation details, the results might differ slightly.

• Added multiple trained models.
• Added a much simpler way to compose network from mainstream classification networks (resnet, inception...) and Guide.
• Update to the latest version according to caffe version, with 5% mAP increase.
• Use C++ record iterator based on back-end multi-thread engine to achieve huge speed up on multi-gpu environments.
• Monitor validation mAP during training.
• More network symbols under development and test.
• Extra operators are now in mxnet/src/operator/contrib, symbols are modified. Please use Release-v0.2-beta for old models.